Search Results for author: Moshe Salhov

Found 12 papers, 2 papers with code

Imbalanced Classification via a Tabular Translation GAN

no code implementations19 Apr 2022 Jonathan Gradstein, Moshe Salhov, Yoav Tulpan, Ofir Lindenbaum, Amir Averbuch

When presented with a binary classification problem where the data exhibits severe class imbalance, most standard predictive methods may fail to accurately model the minority class.

Classification imbalanced classification +1

Search and Score-based Waterfall Auction Optimization

1 code implementation17 Jan 2022 Dan Halbersberg, Matan Halevi, Moshe Salhov

Our framework guarantees that the waterfall revenue improves between iterations ultimately converging into a local optimum.

L0-Sparse Canonical Correlation Analysis

no code implementations ICLR 2022 Ofir Lindenbaum, Moshe Salhov, Amir Averbuch, Yuval Kluger

We further propose $\ell_0$-Deep CCA for solving the problem of non-linear sparse CCA by modeling the correlated representations using deep nets.

$\ell_0$-based Sparse Canonical Correlation Analysis

1 code implementation12 Oct 2020 Ofir Lindenbaum, Moshe Salhov, Amir Averbuch, Yuval Kluger

We further propose $\ell_0$-Deep CCA for solving the problem of non-linear sparse CCA by modeling the correlated representations using deep nets.

Deep Gated Canonical Correlation Analysis

no code implementations28 Sep 2020 Ofir Lindenbaum, Moshe Salhov, Amir Averbuch, Yuval Kluger

The proposed procedure learns two non-linear transformations and simultaneously gates the input variables to identify a subset of most correlated variables.

Majority Voting and the Condorcet's Jury Theorem

no code implementations8 Feb 2020 Hanan Shteingart, Eran Marom, Igor Itkin, Gil Shabat, Michael Kolomenkin, Moshe Salhov, Liran Katzir

There is a striking relationship between a three hundred years old Political Science theorem named "Condorcet's jury theorem" (1785), which states that majorities are more likely to choose correctly when individual votes are often correct and independent, and a modern Machine Learning concept called "Strength of Weak Learnability" (1990), which describes a method for converting a weak learning algorithm into one that achieves arbitrarily high accuracy and stands in the basis of Ensemble Learning.

Ensemble Learning

Kernel Scaling for Manifold Learning and Classification

no code implementations4 Jul 2017 Ofir Lindenbaum, Moshe Salhov, Arie Yeredor, Amir Averbuch

We propose to set a scale parameter that is tailored to one of two types of tasks: classification and manifold learning.

Classification Dimensionality Reduction +1

Incomplete Pivoted QR-based Dimensionality Reduction

no code implementations12 Jul 2016 Amit Bermanis, Aviv Rotbart, Moshe Salhov, Amir Averbuch

The dictionary enables to have a natural extension of the low-dimensional embedding to out-of-sample data points, which gives rise to a distortion-based criterion for anomaly detection.

Anomaly Detection Collaborative Filtering +2

Multi-View Kernel Consensus For Data Analysis

no code implementations28 Jun 2016 Moshe Salhov, Ofir Lindenbaum, Yariv Aizenbud, Avi Silberschatz, Yoel Shkolnisky, Amir Averbuch

Data analysis methods aim to uncover the underlying low dimensional structure imposed by the low dimensional hidden parameters by utilizing distance metrics that consider the set of attributes as a single monolithic set.

Diffusion Representations

no code implementations19 Nov 2015 Moshe Salhov, Amit Bermanis, Guy Wolf, Amir Averbuch

In this paper, we present a representation framework for data analysis of datasets that is based on a closed-form decomposition of the measure-based kernel.

MultiView Diffusion Maps

no code implementations23 Aug 2015 Ofir Lindenbaum, Arie Yeredor, Moshe Salhov, Amir Averbuch

The multi-view dimensionality reduction is achieved by defining a cross-view model in which an implied random walk process is restrained to hop between objects in the different views.

Anomaly Detection Dimensionality Reduction

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